Zero-Shot Text-to-Speech from Continuous Text Streams

ICLR 2025 Conference Submission1359 Authors

Keywords: zero-shot text-to-speech, streaming, state-space models

TL;DR: We introduce a zero-shot text-to-speech model that is capable of generating speech from a continuous text stream, with support for on-device streaming.

Abstract: Existing zero-shot text-to-speech (TTS) systems are typically designed to process complete sentences and are constrained by the maximum duration for which they have been trained. However, in many streaming applications, texts arrive continuously in short chunks, necessitating instant responses from the system. We identify the essential capabilities required for chunk-level streaming and introduce L3Speech, a stream-aware model that supports non-stop speech generation, text-audio stream synchronization, and seamless transitions between short speech chunks. To achieve these, we propose (1) adopting Mamba, a class of sequence modeling distinguished by linear-time decoding, which is augmented by cross-attention mechanisms for conditioning, (2) utilizing rotary positional embeddings in the computation of cross-attention, enabling the model to process a non-stop text stream by sliding a window, and (3) decoding with semantic guidance, a technique that aligns speech with the transcript during inference with minimal overhead. Experimental results demonstrate that our models are competitive with state-of-the-art language model-based zero-shot TTS models, while also providing flexibility to support a wide range of streaming scenarios.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1359